Before you start

Set my seed

# Any number can be chose
set.seed(567890)

Goals for this file

Use raw fastq and generate the quality plots to asses the quality of reads
Filter and trim out bad sequences and bases from our sequencing files
Write out fastq files with high quality sequences
Evaluate the quality from our filter and trim.
Infer errors on forward and reverse reads individually
Identified ASVs on forward and reverse reads separately using the error model.
Merge forward and reverse ASVs into “contigous ASVs”.
Generate ASV count table. (otu_table input for phyloseq.).

Output that we need:

ASV count table: otu_table
Taxonomy table tax_table
Sample information: sample_table track the reads lost throughout DADA2 workflow.

Load Libraries

#Effecient package loading with pacman
pacman::p_load(tidyverse, devtools, dada2, phyloseq, patchwork, DT,
               install = FALSE)

Load Data

#Set the raw fastq path to the raw sequencing files
#Path to the fastq files
raw_fastqs_path <- "data/01_DADA2/00_trimmed_fastq"
raw_fastqs_path

## [1] "data/01_DADA2/00_trimmed_fastq"

#What files are in this path (Intuition check)
list.files(raw_fastqs_path)

##  [1] "SRR17060816_trim_1.fq.gz" "SRR17060816_trim_2.fq.gz"
##  [3] "SRR17060817_trim_1.fq.gz" "SRR17060817_trim_2.fq.gz"
##  [5] "SRR17060818_trim_1.fq.gz" "SRR17060818_trim_2.fq.gz"
##  [7] "SRR17060819_trim_1.fq.gz" "SRR17060819_trim_2.fq.gz"
##  [9] "SRR17060820_trim_1.fq.gz" "SRR17060820_trim_2.fq.gz"
## [11] "SRR17060821_trim_1.fq.gz" "SRR17060821_trim_2.fq.gz"
## [13] "SRR17060822_trim_1.fq.gz" "SRR17060822_trim_2.fq.gz"
## [15] "SRR17060823_trim_1.fq.gz" "SRR17060823_trim_2.fq.gz"
## [17] "SRR17060824_trim_1.fq.gz" "SRR17060824_trim_2.fq.gz"
## [19] "SRR17060825_trim_1.fq.gz" "SRR17060825_trim_2.fq.gz"
## [21] "SRR17060826_trim_1.fq.gz" "SRR17060826_trim_2.fq.gz"
## [23] "SRR17060827_trim_1.fq.gz" "SRR17060827_trim_2.fq.gz"
## [25] "SRR17060828_trim_1.fq.gz" "SRR17060828_trim_2.fq.gz"
## [27] "SRR17060829_trim_1.fq.gz" "SRR17060829_trim_2.fq.gz"
## [29] "SRR17060830_trim_1.fq.gz" "SRR17060830_trim_2.fq.gz"
## [31] "SRR17060831_trim_1.fq.gz" "SRR17060831_trim_2.fq.gz"
## [33] "SRR17060832_trim_1.fq.gz" "SRR17060832_trim_2.fq.gz"
## [35] "SRR17060833_trim_1.fq.gz" "SRR17060833_trim_2.fq.gz"
## [37] "SRR17060834_trim_1.fq.gz" "SRR17060834_trim_2.fq.gz"
## [39] "SRR17060835_trim_1.fq.gz" "SRR17060835_trim_2.fq.gz"
## [41] "SRR17060836_trim_1.fq.gz" "SRR17060836_trim_2.fq.gz"
## [43] "SRR17060837_trim_1.fq.gz" "SRR17060837_trim_2.fq.gz"
## [45] "SRR17060838_trim_1.fq.gz" "SRR17060838_trim_2.fq.gz"
## [47] "SRR17060839_trim_1.fq.gz" "SRR17060839_trim_2.fq.gz"
## [49] "SRR17060840_trim_1.fq.gz" "SRR17060840_trim_2.fq.gz"
## [51] "SRR17060841_trim_1.fq.gz" "SRR17060841_trim_2.fq.gz"
## [53] "SRR17060842_trim_1.fq.gz" "SRR17060842_trim_2.fq.gz"
## [55] "SRR17060843_trim_1.fq.gz" "SRR17060843_trim_2.fq.gz"
## [57] "SRR17060844_trim_1.fq.gz" "SRR17060844_trim_2.fq.gz"
## [59] "SRR17060845_trim_1.fq.gz" "SRR17060845_trim_2.fq.gz"
## [61] "SRR17060846_trim_1.fq.gz" "SRR17060846_trim_2.fq.gz"
## [63] "SRR17060847_trim_1.fq.gz" "SRR17060847_trim_2.fq.gz"

#How many files are there?
str(list.files(raw_fastqs_path))

##  chr [1:64] "SRR17060816_trim_1.fq.gz" "SRR17060816_trim_2.fq.gz" ...

#Create a vector of forward reads
forward_reads <- list.files(raw_fastqs_path, pattern = "_trim_1.fq.gz", full.names = TRUE) 
#Intuition check
head(forward_reads)

## [1] "data/01_DADA2/00_trimmed_fastq/SRR17060816_trim_1.fq.gz"
## [2] "data/01_DADA2/00_trimmed_fastq/SRR17060817_trim_1.fq.gz"
## [3] "data/01_DADA2/00_trimmed_fastq/SRR17060818_trim_1.fq.gz"
## [4] "data/01_DADA2/00_trimmed_fastq/SRR17060819_trim_1.fq.gz"
## [5] "data/01_DADA2/00_trimmed_fastq/SRR17060820_trim_1.fq.gz"
## [6] "data/01_DADA2/00_trimmed_fastq/SRR17060821_trim_1.fq.gz"

#Create a vector of reverse reads
reverse_reads <-list.files(raw_fastqs_path, pattern = "_trim_2.fq.gz", full.names = TRUE)
#Intuition check
head(reverse_reads)

## [1] "data/01_DADA2/00_trimmed_fastq/SRR17060816_trim_2.fq.gz"
## [2] "data/01_DADA2/00_trimmed_fastq/SRR17060817_trim_2.fq.gz"
## [3] "data/01_DADA2/00_trimmed_fastq/SRR17060818_trim_2.fq.gz"
## [4] "data/01_DADA2/00_trimmed_fastq/SRR17060819_trim_2.fq.gz"
## [5] "data/01_DADA2/00_trimmed_fastq/SRR17060820_trim_2.fq.gz"
## [6] "data/01_DADA2/00_trimmed_fastq/SRR17060821_trim_2.fq.gz"

Raw Quality plots

# Randomly select 12 samples from dataset to evaluate 
# Selecting 12 is typically better than 2 (like we did in class for efficiency)
random_samples <- sample(1:length(reverse_reads), size = 12)
random_samples

##  [1] 16 22 15  1 14  6 30 27 11 13 23 32

# Calculate and plot quality of these two samples
forward_filteredQual_plot_12 <- plotQualityProfile(forward_reads[random_samples]) + 
  labs(title = "Forward Read: Raw Quality")

reverse_filteredQual_plot_12 <- plotQualityProfile(reverse_reads[random_samples]) + 
  labs(title = "Reverse Read: Raw Quality")

# Plot them together with patchwork
forward_filteredQual_plot_12 + reverse_filteredQual_plot_12

Aggregated Raw Quality Plots

# Aggregate all QC plots 
# Forward reads
forward_preQC_plot <- 
  plotQualityProfile(forward_reads, aggregate = TRUE) + 
  labs(title = "Forward Pre-QC")

# reverse reads
reverse_preQC_plot <- 
  plotQualityProfile(reverse_reads, aggregate = TRUE) + 
  labs(title = "Reverse Pre-QC")

preQC_aggregate_plot <- 
  # Plot the forward and reverse together 
  forward_preQC_plot + reverse_preQC_plot

# Show the plot
preQC_aggregate_plot

Prepare a placeholder for filtered reads

# vector of our samples, extract the sample information from our file
samples <- sapply(strsplit(basename(forward_reads), "_"), `[`,1)
#Intuition check
head(samples)

## [1] "SRR17060816" "SRR17060817" "SRR17060818" "SRR17060819" "SRR17060820"
## [6] "SRR17060821"

#place filtered reads into filtered_fastqs_path
filtered_fastqs_path <- "data/01_DADA2/02_filtered_fastqs"
filtered_fastqs_path

## [1] "data/01_DADA2/02_filtered_fastqs"

# create 2 variables : filtered_F, filtered_R
filtered_forward_reads <- 
  file.path(filtered_fastqs_path, paste0(samples, "_R1_filtered.fastq.gz"))

#Intuition check
head(filtered_forward_reads)

## [1] "data/01_DADA2/02_filtered_fastqs/SRR17060816_R1_filtered.fastq.gz"
## [2] "data/01_DADA2/02_filtered_fastqs/SRR17060817_R1_filtered.fastq.gz"
## [3] "data/01_DADA2/02_filtered_fastqs/SRR17060818_R1_filtered.fastq.gz"
## [4] "data/01_DADA2/02_filtered_fastqs/SRR17060819_R1_filtered.fastq.gz"
## [5] "data/01_DADA2/02_filtered_fastqs/SRR17060820_R1_filtered.fastq.gz"
## [6] "data/01_DADA2/02_filtered_fastqs/SRR17060821_R1_filtered.fastq.gz"

length(filtered_forward_reads)

## [1] 32

filtered_reverse_reads <- file.path(filtered_fastqs_path, paste0(samples,
                                                  "_R2_filtered.fastq.gz"))
#Intuition check
length(filtered_reverse_reads)

## [1] 32

Filter and Trim Reads

Parameters of filter and trim DEPEND ON THE DATASET

maxN = number of N bases. Remove all Ns from the data.
maxEE = quality filtering threshold applied to expected errors. By default, all expected errors. Mar recommends using c(1,1). Here, if there is maxEE expected errors, its okay. If more, throw away sequence.
trimLeft = trim certain number of base pairs on start of each read
truncQ = truncate reads at the first instance of a quality score less than or equal to selected number. Chose 2
rm.phix = remove phi x
compress = make filtered files .gzipped
multithread = multithread

#Assign a vector to filtered reads
#Trim out poor bases
#Write out filtered fastq files
filtered_reads <-
  filterAndTrim(fwd = forward_reads, filt = filtered_forward_reads,
              rev = reverse_reads, filt.rev = filtered_reverse_reads,
               trimLeft = c(9,9),
              maxN = 0, maxEE = c(1, 1),truncQ = 2, rm.phix = TRUE,
              compress = TRUE, multithread = 6)

Trimmed Quality Plots

# Plot the 12 random samples after QC
forward_filteredQual_plot_12 <- 
  plotQualityProfile(filtered_forward_reads[random_samples]) + 
  labs(title = "Trimmed Forward Read Quality")

reverse_filteredQual_plot_12 <- 
  plotQualityProfile(filtered_reverse_reads[random_samples]) + 
  labs(title = "Trimmed Reverse Read Quality")

# Put the two plots together 
forward_filteredQual_plot_12 + reverse_filteredQual_plot_12

Aggregated Trimmed Plots

# Aggregate all QC plots 
# Forward reads
forward_postQC_plot <- 
  plotQualityProfile(filtered_forward_reads, aggregate = TRUE) + 
  labs(title = "Forward Post-QC")

# reverse reads
reverse_postQC_plot <- 
  plotQualityProfile(filtered_reverse_reads, aggregate = TRUE) + 
  labs(title = "Reverse Post-QC")

postQC_aggregate_plot <- 
  # Plot the forward and reverse together 
  forward_postQC_plot + reverse_postQC_plot

# Show the plot
postQC_aggregate_plot

Stats on read output from `filterAndTrim`

#Make output into dataframe
filtered_df <- as.data.frame(filtered_reads)
head(filtered_df)

##                          reads.in reads.out
## SRR17060816_trim_1.fq.gz   285558      1647
## SRR17060817_trim_1.fq.gz   676817       504
## SRR17060818_trim_1.fq.gz   591364       608
## SRR17060819_trim_1.fq.gz   379452      1683
## SRR17060820_trim_1.fq.gz   570270      1040
## SRR17060821_trim_1.fq.gz   556682      1205

# calculate some stats
filtered_df %>%
  reframe(median_reads_in = median(reads.in),
          median_reads_out = median(reads.out),
          median_percent_retained = (median(reads.out)/median(reads.in)))

##   median_reads_in median_reads_out median_percent_retained
## 1        294748.5           1175.5             0.003988146

[Insert paragraph interpreting the results above]

How many reads got through? Is it “enough”?
Should you play with the parameters in filterAndTrim() more? If so, which parameters?

Error Modeling

Note every sequencing run needs to be run separately! The error model MUST be run separately on each illumina dataset. If you’d like to combine the datasets from multiple sequencing runs, you’ll need to do the exact same filterAndTrim() step AND, very importantly, you’ll need to have the same primer and ASV length expected by the output.

Infer error rates for all possible transitions within purines and pyrimidines (A<>G or C<>T) and transversions between all purine and pyrimidine combinations.

Error model is learned by alternating estimation of the error rates and inference of sample composition until they converge.

Starts with the assumption that the error rates are the maximum (takes the most abundant sequence (“center”) and assumes it’s the only sequence not caused by errors).
Compares the other sequences to the most abundant sequence.
Uses at most 10⁸ nucleotides for the error estimation.
Uses parametric error estimation function of loess fit on the observed error rates.

#Forward reads
error_forward_reads <-
  learnErrors(filtered_forward_reads, multithread = TRUE)

## 15852739 total bases in 205108 reads from 32 samples will be used for learning the error rates.

#Plot forward reads errors
forward_error_plot <-
  plotErrors(error_forward_reads, nominalQ = TRUE) + 
  labs(title =     "Forward Read Error Model")

#Reverse reads
error_reverse_reads <-
  learnErrors(filtered_reverse_reads, multithread = TRUE)

## 50042640 total bases in 205108 reads from 32 samples will be used for learning the error rates.

#Plot reverse reads errors
reverse_error_plot <-
  plotErrors(error_reverse_reads, nominalQ = TRUE) +
    labs(title = "Reverse Read Error Model")

#Put the two plots together
forward_error_plot + reverse_error_plot

## Warning in scale_y_log10(): log-10 transformation introduced infinite values.
## log-10 transformation introduced infinite values.
## log-10 transformation introduced infinite values.

[Insert paragraph interpreting the plot above above]

The error rates for each possible transition (A→C, A→G, …) are shown in the plot above.

Details of the plot: - Points: The observed error rates for each consensus quality score.
- Black line: Estimated error rates after convergence of the machine-learning algorithm.
- Red line: The error rates expected under the nominal definition of the Q-score.

Similar to what is mentioned in the dada2 tutorial: the estimated error rates (black line) are a “reasonably good” fit to the observed rates (points), and the error rates drop with increased quality as expected. We can now infer ASVs!

Infer ASVs

An important note: This process occurs separately on forward and reverse reads! This is quite a different approach from how OTUs are identified in Mothur and also from UCHIME, oligotyping, and other OTU, MED, and ASV approaches.

#Infer forward ASVs
dada_forward <- dada(filtered_forward_reads, 
                     err = error_forward_reads,
                     multithread = 6)

## Sample 1 - 1647 reads in 457 unique sequences.
## Sample 2 - 504 reads in 127 unique sequences.
## Sample 3 - 608 reads in 128 unique sequences.
## Sample 4 - 1683 reads in 422 unique sequences.
## Sample 5 - 1040 reads in 219 unique sequences.
## Sample 6 - 1205 reads in 207 unique sequences.
## Sample 7 - 21510 reads in 4837 unique sequences.
## Sample 8 - 23278 reads in 6796 unique sequences.
## Sample 9 - 716 reads in 198 unique sequences.
## Sample 10 - 15477 reads in 4084 unique sequences.
## Sample 11 - 12131 reads in 3402 unique sequences.
## Sample 12 - 7779 reads in 2069 unique sequences.
## Sample 13 - 16122 reads in 3549 unique sequences.
## Sample 14 - 475 reads in 316 unique sequences.
## Sample 15 - 53 reads in 36 unique sequences.
## Sample 16 - 779 reads in 233 unique sequences.
## Sample 17 - 511 reads in 184 unique sequences.
## Sample 18 - 933 reads in 219 unique sequences.
## Sample 19 - 1417 reads in 430 unique sequences.
## Sample 20 - 4163 reads in 577 unique sequences.
## Sample 21 - 503 reads in 105 unique sequences.
## Sample 22 - 707 reads in 313 unique sequences.
## Sample 23 - 901 reads in 134 unique sequences.
## Sample 24 - 570 reads in 246 unique sequences.
## Sample 25 - 21855 reads in 3740 unique sequences.
## Sample 26 - 17393 reads in 2483 unique sequences.
## Sample 27 - 18560 reads in 3128 unique sequences.
## Sample 28 - 16563 reads in 3851 unique sequences.
## Sample 29 - 13970 reads in 4775 unique sequences.
## Sample 30 - 561 reads in 212 unique sequences.
## Sample 31 - 1146 reads in 281 unique sequences.
## Sample 32 - 348 reads in 131 unique sequences.

#Infer reverse ASVs
dada_reverse <- dada(filtered_reverse_reads, 
                     err = error_reverse_reads, 
                     multithread = 6)

## Sample 1 - 1647 reads in 892 unique sequences.
## Sample 2 - 504 reads in 294 unique sequences.
## Sample 3 - 608 reads in 308 unique sequences.
## Sample 4 - 1683 reads in 911 unique sequences.
## Sample 5 - 1040 reads in 518 unique sequences.
## Sample 6 - 1205 reads in 537 unique sequences.
## Sample 7 - 21510 reads in 6987 unique sequences.
## Sample 8 - 23278 reads in 10427 unique sequences.
## Sample 9 - 716 reads in 422 unique sequences.
## Sample 10 - 15477 reads in 6258 unique sequences.
## Sample 11 - 12131 reads in 4782 unique sequences.
## Sample 12 - 7779 reads in 2775 unique sequences.
## Sample 13 - 16122 reads in 6011 unique sequences.
## Sample 14 - 475 reads in 356 unique sequences.
## Sample 15 - 53 reads in 48 unique sequences.
## Sample 16 - 779 reads in 466 unique sequences.
## Sample 17 - 511 reads in 321 unique sequences.
## Sample 18 - 933 reads in 499 unique sequences.
## Sample 19 - 1417 reads in 824 unique sequences.
## Sample 20 - 4163 reads in 1783 unique sequences.
## Sample 21 - 503 reads in 243 unique sequences.
## Sample 22 - 707 reads in 482 unique sequences.
## Sample 23 - 901 reads in 416 unique sequences.
## Sample 24 - 570 reads in 289 unique sequences.
## Sample 25 - 21855 reads in 7029 unique sequences.
## Sample 26 - 17393 reads in 4709 unique sequences.
## Sample 27 - 18560 reads in 4340 unique sequences.
## Sample 28 - 16563 reads in 5132 unique sequences.
## Sample 29 - 13970 reads in 6762 unique sequences.
## Sample 30 - 561 reads in 296 unique sequences.
## Sample 31 - 1146 reads in 658 unique sequences.
## Sample 32 - 348 reads in 248 unique sequences.

#Inspect
dada_forward[1]

## $SRR17060816_R1_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 47 sequence variants were inferred from 457 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

dada_reverse[1]

## $SRR17060816_R2_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 44 sequence variants were inferred from 892 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

dada_forward[12]

## $SRR17060827_R1_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 22 sequence variants were inferred from 2069 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

dada_reverse[12]

## $SRR17060827_R2_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 32 sequence variants were inferred from 2775 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

Merge Forward and Reverse ASVs

Now, merge the forward and reverse ASVs into contigs.

# merge forward and reverse ASVs
merged_ASVs <- mergePairs(dada_forward, filtered_forward_reads, 
                          dada_reverse, filtered_reverse_reads,
                          verbose = TRUE)

## 1383 paired-reads (in 36 unique pairings) successfully merged out of 1509 (in 67 pairings) input.

## 339 paired-reads (in 11 unique pairings) successfully merged out of 431 (in 28 pairings) input.

## 477 paired-reads (in 11 unique pairings) successfully merged out of 543 (in 20 pairings) input.

## 1288 paired-reads (in 40 unique pairings) successfully merged out of 1533 (in 83 pairings) input.

## 872 paired-reads (in 23 unique pairings) successfully merged out of 972 (in 37 pairings) input.

## 1000 paired-reads (in 18 unique pairings) successfully merged out of 1139 (in 33 pairings) input.

## 19897 paired-reads (in 76 unique pairings) successfully merged out of 21412 (in 160 pairings) input.

## 20943 paired-reads (in 194 unique pairings) successfully merged out of 22848 (in 504 pairings) input.

## 495 paired-reads (in 15 unique pairings) successfully merged out of 660 (in 33 pairings) input.

## 14654 paired-reads (in 65 unique pairings) successfully merged out of 15285 (in 194 pairings) input.

## 11065 paired-reads (in 91 unique pairings) successfully merged out of 11704 (in 209 pairings) input.

## 7440 paired-reads (in 25 unique pairings) successfully merged out of 7675 (in 73 pairings) input.

## 14703 paired-reads (in 49 unique pairings) successfully merged out of 16010 (in 138 pairings) input.

## 166 paired-reads (in 9 unique pairings) successfully merged out of 336 (in 26 pairings) input.

## 16 paired-reads (in 2 unique pairings) successfully merged out of 16 (in 2 pairings) input.

## 570 paired-reads (in 19 unique pairings) successfully merged out of 676 (in 36 pairings) input.

## 340 paired-reads (in 13 unique pairings) successfully merged out of 419 (in 28 pairings) input.

## 716 paired-reads (in 14 unique pairings) successfully merged out of 861 (in 30 pairings) input.

## 1060 paired-reads (in 27 unique pairings) successfully merged out of 1161 (in 61 pairings) input.

## 3779 paired-reads (in 61 unique pairings) successfully merged out of 3953 (in 101 pairings) input.

## 416 paired-reads (in 5 unique pairings) successfully merged out of 445 (in 16 pairings) input.

## 381 paired-reads (in 20 unique pairings) successfully merged out of 482 (in 35 pairings) input.

## 748 paired-reads (in 13 unique pairings) successfully merged out of 865 (in 24 pairings) input.

## 499 paired-reads (in 11 unique pairings) successfully merged out of 519 (in 16 pairings) input.

## 20849 paired-reads (in 54 unique pairings) successfully merged out of 21601 (in 172 pairings) input.

## 16873 paired-reads (in 45 unique pairings) successfully merged out of 17250 (in 85 pairings) input.

## 17389 paired-reads (in 40 unique pairings) successfully merged out of 18404 (in 88 pairings) input.

## 13718 paired-reads (in 48 unique pairings) successfully merged out of 16285 (in 135 pairings) input.

## 11839 paired-reads (in 128 unique pairings) successfully merged out of 13430 (in 446 pairings) input.

## 478 paired-reads (in 10 unique pairings) successfully merged out of 525 (in 16 pairings) input.

## 777 paired-reads (in 26 unique pairings) successfully merged out of 1084 (in 47 pairings) input.

## 168 paired-reads (in 10 unique pairings) successfully merged out of 278 (in 26 pairings) input.

# Evaluate the output 
typeof(merged_ASVs)

## [1] "list"

length(merged_ASVs)

## [1] 32

names(merged_ASVs)

##  [1] "SRR17060816_R1_filtered.fastq.gz" "SRR17060817_R1_filtered.fastq.gz"
##  [3] "SRR17060818_R1_filtered.fastq.gz" "SRR17060819_R1_filtered.fastq.gz"
##  [5] "SRR17060820_R1_filtered.fastq.gz" "SRR17060821_R1_filtered.fastq.gz"
##  [7] "SRR17060822_R1_filtered.fastq.gz" "SRR17060823_R1_filtered.fastq.gz"
##  [9] "SRR17060824_R1_filtered.fastq.gz" "SRR17060825_R1_filtered.fastq.gz"
## [11] "SRR17060826_R1_filtered.fastq.gz" "SRR17060827_R1_filtered.fastq.gz"
## [13] "SRR17060828_R1_filtered.fastq.gz" "SRR17060829_R1_filtered.fastq.gz"
## [15] "SRR17060830_R1_filtered.fastq.gz" "SRR17060831_R1_filtered.fastq.gz"
## [17] "SRR17060832_R1_filtered.fastq.gz" "SRR17060833_R1_filtered.fastq.gz"
## [19] "SRR17060834_R1_filtered.fastq.gz" "SRR17060835_R1_filtered.fastq.gz"
## [21] "SRR17060836_R1_filtered.fastq.gz" "SRR17060837_R1_filtered.fastq.gz"
## [23] "SRR17060838_R1_filtered.fastq.gz" "SRR17060839_R1_filtered.fastq.gz"
## [25] "SRR17060840_R1_filtered.fastq.gz" "SRR17060841_R1_filtered.fastq.gz"
## [27] "SRR17060842_R1_filtered.fastq.gz" "SRR17060843_R1_filtered.fastq.gz"
## [29] "SRR17060844_R1_filtered.fastq.gz" "SRR17060845_R1_filtered.fastq.gz"
## [31] "SRR17060846_R1_filtered.fastq.gz" "SRR17060847_R1_filtered.fastq.gz"

# Inspect the merger data.frame from the 20210602-MA-ABB1P 
#head(merged_ASVs[[3]])

Create Raw ASV Count Table

# Create the ASV Count Table 
raw_ASV_table <- makeSequenceTable(merged_ASVs)

# Write out the file to data/01_DADA2


# Check the type and dimensions of the data
dim(raw_ASV_table)

## [1]  32 663

class(raw_ASV_table)

## [1] "matrix" "array"

typeof(raw_ASV_table)

## [1] "integer"

# Inspect the distribution of sequence lengths of all ASVs in dataset 
table(nchar(getSequences(raw_ASV_table)))

## 
## 108 114 152 159 171 186 187 190 199 215 226 227 228 242 272 273 285 300 
##   1   1   4   1   1   6   1   1   1   1   1   1   2 446 185   5   2   3

# Inspect the distribution of sequence lengths of all ASVs in dataset 
# AFTER TRIM
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table))) %>%
  ggplot(aes(x = Seq_Length )) + 
  geom_histogram() + 
  labs(title = "Raw distribution of ASV length")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

###################################################
###################################################
# TRIM THE ASVS
# Let's trim the ASVs to only be the right size, which is 249.
# 249 originates from our expected amplicon of 252 - 3bp in the forward read due to low quality.

# We will allow for a few 
raw_ASV_table_trimmed <- raw_ASV_table[,nchar(colnames(raw_ASV_table)) %in% 242]

# Inspect the distribution of sequence lengths of all ASVs in dataset 
table(nchar(getSequences(raw_ASV_table_trimmed)))

## 
## 242 
## 446

# What proportion is left of the sequences? 
sum(raw_ASV_table_trimmed)/sum(raw_ASV_table)

## [1] 0.920011

# Inspect the distribution of sequence lengths of all ASVs in dataset 
# AFTER TRIM
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table_trimmed))) %>%
  ggplot(aes(x = Seq_Length )) + 
  geom_histogram() + 
  labs(title = "Trimmed distribution of ASV length")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Note the peak at 249 is ABOVE 3000

# Let's zoom in on the plot 
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table_trimmed))) %>%
  ggplot(aes(x = Seq_Length )) + 
  geom_histogram() + 
  labs(title = "Trimmed distribution of ASV length") + 
  scale_y_continuous(limits = c(0, 500))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Taking into account the lower, zoomed-in plot. Do we want to remove those extra ASVs?

Remove Chimeras

Sometimes chimeras arise in our workflow.

Chimeric sequences are artificial sequences formed by the combination of two or more distinct biological sequences. These chimeric sequences can arise during the polymerase chain reaction (PCR) amplification step of the 16S rRNA gene, where fragments from different templates can be erroneously joined together.

Chimera removal is an essential step in the analysis of 16S sequencing data to improve the accuracy of downstream analyses, such as taxonomic assignment and diversity assessment. It helps to avoid the inclusion of misleading or spurious sequences that could lead to incorrect biological interpretations.

# Remove the chimeras in the raw ASV table
noChimeras_ASV_table <- removeBimeraDenovo(raw_ASV_table_trimmed, 
                                           method="consensus", 
                                           multithread=TRUE, verbose=TRUE)

## Identified 21 bimeras out of 446 input sequences.

# Check the dimensions
dim(noChimeras_ASV_table)

## [1]  32 425

# What proportion is left of the sequences? 
sum(noChimeras_ASV_table)/sum(raw_ASV_table_trimmed)

## [1] 0.9922411

sum(noChimeras_ASV_table)/sum(raw_ASV_table)

## [1] 0.9128727

# Plot it 
data.frame(Seq_Length_NoChim = nchar(getSequences(noChimeras_ASV_table))) %>%
  ggplot(aes(x = Seq_Length_NoChim )) + 
  geom_histogram()+ 
  labs(title = "Trimmed + Chimera Removal distribution of ASV length")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Note the difference in the peak at 249, which is now BELOW 3000

Track the read counts

Here, we will look at the number of reads that were lost in the filtering, denoising, merging, and chimera removal.

# A little function to identify number seqs 
getN <- function(x) sum(getUniques(x))

# Make the table to track the seqs 
track <- cbind(filtered_reads, 
               sapply(dada_forward, getN),
               sapply(dada_reverse, getN),
               sapply(merged_ASVs, getN),
               rowSums(noChimeras_ASV_table))

head(track)

##                          reads.in reads.out                 
## SRR17060816_trim_1.fq.gz   285558      1647 1608 1518 1383 0
## SRR17060817_trim_1.fq.gz   676817       504  468  441  339 0
## SRR17060818_trim_1.fq.gz   591364       608  589  546  477 0
## SRR17060819_trim_1.fq.gz   379452      1683 1609 1554 1288 0
## SRR17060820_trim_1.fq.gz   570270      1040 1006  994  872 0
## SRR17060821_trim_1.fq.gz   556682      1205 1177 1142 1000 0

# Update column names to be more informative (most are missing at the moment!)
colnames(track) <- c("input", "filtered", "denoisedF", "denoisedR", "merged", "nochim")
rownames(track) <- samples

# Generate a dataframe to track the reads through our DADA2 pipeline
track_counts_df <- 
  track %>%
  # make it a dataframe
  as.data.frame() %>%
  rownames_to_column(var = "names") %>%
  mutate(perc_reads_retained = 100 * nochim / input)

# Visualize it in table format 
DT::datatable(track_counts_df)

# Plot it!
track_counts_df %>%
  pivot_longer(input:nochim, names_to = "read_type", values_to = "num_reads") %>%
  mutate(read_type = fct_relevel(read_type, 
                                 "input", "filtered", "denoisedF", "denoisedR", "merged", "nochim")) %>%
  ggplot(aes(x = read_type, y = num_reads, fill = read_type)) + 
  geom_line(aes(group = names), color = "grey") + 
  geom_point(shape = 21, size = 3, alpha = 0.8) + 
  scale_fill_brewer(palette = "Spectral") + 
  labs(x = "Filtering Step", y = "Number of Sequences") + 
  theme_bw()

Assign Taxonomy

Here, we will use the silva database version 138!

# The next line took 2 mins to run
taxa_train <- 
  assignTaxonomy(noChimeras_ASV_table, 
                 "/workdir/in_class_data/taxonomy/silva_nr99_v138.1_train_set.fa.gz", 
                 multithread=TRUE)

# the next line took 3 minutes 
taxa_addSpecies <- 
  addSpecies(taxa_train, 
             "/workdir/in_class_data/taxonomy/silva_species_assignment_v138.1.fa.gz")

# Inspect the taxonomy 
taxa_print <- taxa_addSpecies # Removing sequence rownames for display only
rownames(taxa_print) <- NULL
#View(taxa_print)

Prepare the data for export!

1. ASV Table

Below, we will prepare the following:

Two ASV Count tables:
1. With ASV seqs: ASV headers include the entire ASV sequence ~250bps.
2. with ASV names: This includes re-written and shortened headers like ASV_1, ASV_2, etc, which will match the names in our fasta file below.
ASV_fastas: A fasta file that we can use to build a tree for phylogenetic analyses (e.g. phylogenetic alpha diversity metrics or UNIFRAC dissimilarty).

Finalize ASV Count Tables

########### 2. COUNT TABLE ###############
############## Modify the ASV names and then save a fasta file!  ############## 
# Give headers more manageable names
# First pull the ASV sequences
asv_seqs <- colnames(noChimeras_ASV_table)
asv_seqs[1:5]

## [1] "CGGAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGCATGCAGGCGGTTTGTTAAGCAAGATGTGAAAGCCCCGGGCTTAACCTGGGAATCGCATTTTGAACTGGCAAGCTAGAGTCTTGTAGAGGGGGGTAGAATTTCAGGTGTAGCGGTGAAATGCGTAGAGATCTGAAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGATGCGAAAGCGTGGGGA"
## [2] "CGGAGGATCCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTCTCTTAAGTCAGCGTTGAAAGTTTTCGGCTCAACCGGAAAATTGGCATTGAAACTGGGAGACTTGAGTGTAAATGAAGTTGGCGGAATTCGTTGTGTAGCGGTGAAATGCATAGATATAACGAAGAACTCCGATTGCGAAGGCAGCTGACTAACATACAACTGACGCTGAGGCACGAAAGCGTGGGGA"
## [3] "CGGAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGCATGCAGGCGGTTTGTTAAGCAAGATGTGAAAGCCCCGGGCTTAACCTGGGAATTGCATTTTGAACTGGCAAGCTAGAGTCTTGTAGAGGGGGGTAGAATTTCAGGTGTAGCGGTGAAATGCGTAGAGATCTGAAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGATGCGAAAGCGTGGGGA"
## [4] "CGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTCCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCCAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA"
## [5] "CGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACGGCATCCAAAACTGAGAGGCTCGAGTGCGGAAGAGGAGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACACTCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGA"

# make headers for our ASV seq fasta file, which will be our asv names
asv_headers <- vector(dim(noChimeras_ASV_table)[2], mode = "character")
asv_headers[1:5]

## [1] "" "" "" "" ""

# loop through vector and fill it in with ASV names 
for (i in 1:dim(noChimeras_ASV_table)[2]) {
  asv_headers[i] <- paste(">ASV", i, sep = "_")
}

# intitution check
asv_headers[1:5]

## [1] ">ASV_1" ">ASV_2" ">ASV_3" ">ASV_4" ">ASV_5"

##### Rename ASVs in table then write out our ASV fasta file! 
#View(noChimeras_ASV_table)
asv_tab <- t(noChimeras_ASV_table)
#View(asv_tab)

## Rename our asvs! 
row.names(asv_tab) <- sub(">", "", asv_headers)
#View(asv_tab)

2. Taxonomy Table

# Inspect the taxonomy table
#View(taxa_addSpecies)

##### Prepare tax table 
# Add the ASV sequences from the rownames to a column 
new_tax_tab <- 
  taxa_addSpecies%>%
  as.data.frame() %>%
  rownames_to_column(var = "ASVseqs") 
head(new_tax_tab)

##                                                                                                                                                                                                                                              ASVseqs
## 1 CGGAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGCATGCAGGCGGTTTGTTAAGCAAGATGTGAAAGCCCCGGGCTTAACCTGGGAATCGCATTTTGAACTGGCAAGCTAGAGTCTTGTAGAGGGGGGTAGAATTTCAGGTGTAGCGGTGAAATGCGTAGAGATCTGAAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGATGCGAAAGCGTGGGGA
## 2 CGGAGGATCCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTCTCTTAAGTCAGCGTTGAAAGTTTTCGGCTCAACCGGAAAATTGGCATTGAAACTGGGAGACTTGAGTGTAAATGAAGTTGGCGGAATTCGTTGTGTAGCGGTGAAATGCATAGATATAACGAAGAACTCCGATTGCGAAGGCAGCTGACTAACATACAACTGACGCTGAGGCACGAAAGCGTGGGGA
## 3 CGGAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGCATGCAGGCGGTTTGTTAAGCAAGATGTGAAAGCCCCGGGCTTAACCTGGGAATTGCATTTTGAACTGGCAAGCTAGAGTCTTGTAGAGGGGGGTAGAATTTCAGGTGTAGCGGTGAAATGCGTAGAGATCTGAAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGATGCGAAAGCGTGGGGA
## 4 CGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTCCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCCAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## 5 CGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACGGCATCCAAAACTGAGAGGCTCGAGTGCGGAAGAGGAGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACACTCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGA
## 6 CGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCTTAGTAAGTCAGTGGTGAAATCCCCGAGCTCAACTTGGGAACTGCCATTGAAACTACTAGACTAGAGTATGTGAGAGGATAGTGGAATTCCTAGTGTAGGAGTGAAATCCGTAGATATTAGGAGGAACATCAGTGGCGAAGGCGACTATCTGGCACATAACTGACGCTGAGGTACGAAAGCGTGGGGA
##    Kingdom           Phylum               Class              Order
## 1 Bacteria   Proteobacteria Gammaproteobacteria   Enterobacterales
## 2 Bacteria     Bacteroidota         Bacteroidia      Bacteroidales
## 3 Bacteria   Proteobacteria Gammaproteobacteria   Enterobacterales
## 4 Bacteria    Spirochaetota        Brevinematia     Brevinematales
## 5 Bacteria   Proteobacteria Gammaproteobacteria    Pseudomonadales
## 6 Bacteria Desulfobacterota    Desulfovibrionia Desulfovibrionales
##                Family              Genus Species
## 1        Vibrionaceae       Enterovibrio    <NA>
## 2      Tannerellaceae Macellibacteroides    <NA>
## 3        Vibrionaceae       Enterovibrio    <NA>
## 4     Brevinemataceae          Brevinema    <NA>
## 5 Endozoicomonadaceae     Endozoicomonas    <NA>
## 6 Desulfovibrionaceae               <NA>    <NA>

# intution check 
stopifnot(new_tax_tab$ASVseqs == colnames(noChimeras_ASV_table))

# Now let's add the ASV names 
rownames(new_tax_tab) <- rownames(asv_tab)
head(new_tax_tab)

##                                                                                                                                                                                                                                                  ASVseqs
## ASV_1 CGGAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGCATGCAGGCGGTTTGTTAAGCAAGATGTGAAAGCCCCGGGCTTAACCTGGGAATCGCATTTTGAACTGGCAAGCTAGAGTCTTGTAGAGGGGGGTAGAATTTCAGGTGTAGCGGTGAAATGCGTAGAGATCTGAAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGATGCGAAAGCGTGGGGA
## ASV_2 CGGAGGATCCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTCTCTTAAGTCAGCGTTGAAAGTTTTCGGCTCAACCGGAAAATTGGCATTGAAACTGGGAGACTTGAGTGTAAATGAAGTTGGCGGAATTCGTTGTGTAGCGGTGAAATGCATAGATATAACGAAGAACTCCGATTGCGAAGGCAGCTGACTAACATACAACTGACGCTGAGGCACGAAAGCGTGGGGA
## ASV_3 CGGAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGCATGCAGGCGGTTTGTTAAGCAAGATGTGAAAGCCCCGGGCTTAACCTGGGAATTGCATTTTGAACTGGCAAGCTAGAGTCTTGTAGAGGGGGGTAGAATTTCAGGTGTAGCGGTGAAATGCGTAGAGATCTGAAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGATGCGAAAGCGTGGGGA
## ASV_4 CGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTCCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCCAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## ASV_5 CGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACGGCATCCAAAACTGAGAGGCTCGAGTGCGGAAGAGGAGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACACTCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGA
## ASV_6 CGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCTTAGTAAGTCAGTGGTGAAATCCCCGAGCTCAACTTGGGAACTGCCATTGAAACTACTAGACTAGAGTATGTGAGAGGATAGTGGAATTCCTAGTGTAGGAGTGAAATCCGTAGATATTAGGAGGAACATCAGTGGCGAAGGCGACTATCTGGCACATAACTGACGCTGAGGTACGAAAGCGTGGGGA
##        Kingdom           Phylum               Class              Order
## ASV_1 Bacteria   Proteobacteria Gammaproteobacteria   Enterobacterales
## ASV_2 Bacteria     Bacteroidota         Bacteroidia      Bacteroidales
## ASV_3 Bacteria   Proteobacteria Gammaproteobacteria   Enterobacterales
## ASV_4 Bacteria    Spirochaetota        Brevinematia     Brevinematales
## ASV_5 Bacteria   Proteobacteria Gammaproteobacteria    Pseudomonadales
## ASV_6 Bacteria Desulfobacterota    Desulfovibrionia Desulfovibrionales
##                    Family              Genus Species
## ASV_1        Vibrionaceae       Enterovibrio    <NA>
## ASV_2      Tannerellaceae Macellibacteroides    <NA>
## ASV_3        Vibrionaceae       Enterovibrio    <NA>
## ASV_4     Brevinemataceae          Brevinema    <NA>
## ASV_5 Endozoicomonadaceae     Endozoicomonas    <NA>
## ASV_6 Desulfovibrionaceae               <NA>    <NA>

### Final prep of tax table. Add new column with ASV names 
asv_tax <- 
  new_tax_tab %>%
  # add rownames from count table for phyloseq handoff
  mutate(ASV = rownames(asv_tab)) %>%
  # Resort the columns with select
  dplyr::select(Kingdom, Phylum, Class, Order, Family, Genus, Species, ASV, ASVseqs)

head(asv_tax)

##        Kingdom           Phylum               Class              Order
## ASV_1 Bacteria   Proteobacteria Gammaproteobacteria   Enterobacterales
## ASV_2 Bacteria     Bacteroidota         Bacteroidia      Bacteroidales
## ASV_3 Bacteria   Proteobacteria Gammaproteobacteria   Enterobacterales
## ASV_4 Bacteria    Spirochaetota        Brevinematia     Brevinematales
## ASV_5 Bacteria   Proteobacteria Gammaproteobacteria    Pseudomonadales
## ASV_6 Bacteria Desulfobacterota    Desulfovibrionia Desulfovibrionales
##                    Family              Genus Species   ASV
## ASV_1        Vibrionaceae       Enterovibrio    <NA> ASV_1
## ASV_2      Tannerellaceae Macellibacteroides    <NA> ASV_2
## ASV_3        Vibrionaceae       Enterovibrio    <NA> ASV_3
## ASV_4     Brevinemataceae          Brevinema    <NA> ASV_4
## ASV_5 Endozoicomonadaceae     Endozoicomonas    <NA> ASV_5
## ASV_6 Desulfovibrionaceae               <NA>    <NA> ASV_6
##                                                                                                                                                                                                                                                  ASVseqs
## ASV_1 CGGAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGCATGCAGGCGGTTTGTTAAGCAAGATGTGAAAGCCCCGGGCTTAACCTGGGAATCGCATTTTGAACTGGCAAGCTAGAGTCTTGTAGAGGGGGGTAGAATTTCAGGTGTAGCGGTGAAATGCGTAGAGATCTGAAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGATGCGAAAGCGTGGGGA
## ASV_2 CGGAGGATCCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTCTCTTAAGTCAGCGTTGAAAGTTTTCGGCTCAACCGGAAAATTGGCATTGAAACTGGGAGACTTGAGTGTAAATGAAGTTGGCGGAATTCGTTGTGTAGCGGTGAAATGCATAGATATAACGAAGAACTCCGATTGCGAAGGCAGCTGACTAACATACAACTGACGCTGAGGCACGAAAGCGTGGGGA
## ASV_3 CGGAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGCATGCAGGCGGTTTGTTAAGCAAGATGTGAAAGCCCCGGGCTTAACCTGGGAATTGCATTTTGAACTGGCAAGCTAGAGTCTTGTAGAGGGGGGTAGAATTTCAGGTGTAGCGGTGAAATGCGTAGAGATCTGAAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGATGCGAAAGCGTGGGGA
## ASV_4 CGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTCCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCCAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## ASV_5 CGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACGGCATCCAAAACTGAGAGGCTCGAGTGCGGAAGAGGAGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACACTCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGA
## ASV_6 CGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCTTAGTAAGTCAGTGGTGAAATCCCCGAGCTCAACTTGGGAACTGCCATTGAAACTACTAGACTAGAGTATGTGAGAGGATAGTGGAATTCCTAGTGTAGGAGTGAAATCCGTAGATATTAGGAGGAACATCAGTGGCGAAGGCGACTATCTGGCACATAACTGACGCTGAGGTACGAAAGCGTGGGGA

# Intution check
stopifnot(asv_tax$ASV == rownames(asv_tax), rownames(asv_tax) == rownames(asv_tab))

Write `01_DADA2` files

Now, we will write the files! We will write the following to the data/01_DADA2/ folder. We will save both as files that could be submitted as supplements AND as .RData objects for easy loading into the next steps into R.:

ASV_counts.tsv: ASV count table that has ASV names that are re-written and shortened headers like ASV_1, ASV_2, etc, which will match the names in our fasta file below. This will also be saved as data/01_DADA2/ASV_counts.RData.
ASV_counts_withSeqNames.tsv: This is generated with the data object in this file known as noChimeras_ASV_table. ASV headers include the entire ASV sequence ~250bps. In addition, we will save this as a .RData object as data/01_DADA2/noChimeras_ASV_table.RData as we will use this data in analysis/02_Taxonomic_Assignment.Rmd to assign the taxonomy from the sequence headers.
ASVs.fasta: A fasta file output of the ASV names from ASV_counts.tsv and the sequences from the ASVs in ASV_counts_withSeqNames.tsv. A fasta file that we can use to build a tree for phylogenetic analyses (e.g. phylogenetic alpha diversity metrics or UNIFRAC dissimilarty).
We will also make a copy of ASVs.fasta in data/02_TaxAss_FreshTrain/ to be used for the taxonomy classification in the next step in the workflow.
Write out the taxonomy table
track_read_counts.RData: To track how many reads we lost throughout our workflow that could be used and plotted later. We will add this to the metadata in analysis/02_Taxonomic_Assignment.Rmd.

# FIRST, we will save our output as regular files, which will be useful later on. 
# Save to regular .tsv file 
# Write BOTH the modified and unmodified ASV tables to a file!
# Write count table with ASV numbered names (e.g. ASV_1, ASV_2, etc)
write.table(asv_tab, "data/01_DADA2/ASV_counts.tsv", sep = "\t", quote = FALSE, col.names = NA)
# Write count table with ASV sequence names
write.table(noChimeras_ASV_table, "data/01_DADA2/ASV_counts_withSeqNames.tsv", sep = "\t", quote = FALSE, col.names = NA)
# Write out the fasta file for reference later on for what seq matches what ASV
asv_fasta <- c(rbind(asv_headers, asv_seqs))
# Save to a file!
write(asv_fasta, "data/01_DADA2/ASVs.fasta")


# SECOND, let's save the taxonomy tables 
# Write the table 
write.table(asv_tax, "data/01_DADA2/ASV_taxonomy.tsv", sep = "\t", quote = FALSE, col.names = NA)


# THIRD, let's save to a RData object 
# Each of these files will be used in the analysis/02_Taxonomic_Assignment
# RData objects are for easy loading :) 
save(noChimeras_ASV_table, file = "data/01_DADA2/noChimeras_ASV_table.RData")
save(asv_tab, file = "data/01_DADA2/ASV_counts.RData")
# And save the track_counts_df a R object, which we will merge with metadata information in the next step of the analysis in nalysis/02_Taxonomic_Assignment. 
save(track_counts_df, file = "data/01_DADA2/track_read_counts.RData")

##Session information

#Ensure reproducibility
devtools::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.3.2 (2023-10-31)
##  os       Rocky Linux 9.0 (Blue Onyx)
##  system   x86_64, linux-gnu
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       America/New_York
##  date     2024-04-11
##  pandoc   3.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package              * version    date (UTC) lib source
##  abind                  1.4-5      2016-07-21 [2] CRAN (R 4.3.2)
##  ade4                   1.7-22     2023-02-06 [1] CRAN (R 4.3.2)
##  ape                    5.7-1      2023-03-13 [2] CRAN (R 4.3.2)
##  Biobase                2.62.0     2023-10-24 [2] Bioconductor
##  BiocGenerics           0.48.1     2023-11-01 [2] Bioconductor
##  BiocParallel           1.36.0     2023-10-24 [2] Bioconductor
##  biomformat             1.30.0     2023-10-24 [1] Bioconductor
##  Biostrings             2.70.1     2023-10-25 [2] Bioconductor
##  bitops                 1.0-7      2021-04-24 [2] CRAN (R 4.3.2)
##  bslib                  0.5.1      2023-08-11 [2] CRAN (R 4.3.2)
##  cachem                 1.0.8      2023-05-01 [2] CRAN (R 4.3.2)
##  callr                  3.7.3      2022-11-02 [2] CRAN (R 4.3.2)
##  cli                    3.6.1      2023-03-23 [2] CRAN (R 4.3.2)
##  cluster                2.1.4      2022-08-22 [2] CRAN (R 4.3.2)
##  codetools              0.2-19     2023-02-01 [2] CRAN (R 4.3.2)
##  colorspace             2.1-0      2023-01-23 [2] CRAN (R 4.3.2)
##  crayon                 1.5.2      2022-09-29 [2] CRAN (R 4.3.2)
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## ──────────────────────────────────────────────────────────────────────────────

Infer ASVs with DADA2

Calla Bush St George

2024-04-11

Before you start

Set my seed

Goals for this file

Output that we need:

Load Libraries

Load Data

Raw Quality plots

Aggregated Raw Quality Plots

Prepare a placeholder for filtered reads

Filter and Trim Reads

Trimmed Quality Plots

Aggregated Trimmed Plots

Stats on read output from `filterAndTrim`

Error Modeling

Infer ASVs

Merge Forward and Reverse ASVs

Create Raw ASV Count Table

Remove Chimeras

Track the read counts

Assign Taxonomy

Prepare the data for export!

1. ASV Table

Finalize ASV Count Tables

2. Taxonomy Table

Write `01_DADA2` files

Infer ASVs with DADA2

Calla Bush St George

2024-04-11

Before you start

Set my seed

Goals for this file

Output that we need:

Load Libraries

Load Data

Raw Quality plots

Aggregated Raw Quality Plots

Prepare a placeholder for filtered reads

Filter and Trim Reads

Trimmed Quality Plots

Aggregated Trimmed Plots

Stats on read output from filterAndTrim

Error Modeling

Infer ASVs

Merge Forward and Reverse ASVs

Create Raw ASV Count Table

Remove Chimeras

Track the read counts

Assign Taxonomy

Prepare the data for export!

1. ASV Table

Finalize ASV Count Tables

2. Taxonomy Table

Write 01_DADA2 files

Stats on read output from `filterAndTrim`

Write `01_DADA2` files